A one class KNN for signal identification: a biological case study

Authors:
Vito Di Gesu;Giosue Lo Bosco;Luca Pinello
Affiliations:
Dipartimento di Matematica ed Applicazioni, Universita di Palermo, Via Archirafi 34, 90123 Palermo, Italy.;Dipartimento di Matematica ed Applicazioni, Universita di Palermo, Via Archirafi 34, 90123 Palermo, Italy.;Dipartimento di Matematica ed Applicazioni, Universita di Palermo, Via Archirafi 34, 90123 Palermo, Italy
Venue:
International Journal of Knowledge Engineering and Soft Data Paradigms
Year:
2009

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Abstract

The paper describes an application of a one class KNN to identify different signal patterns embedded in a noise structured background. The problem becomes harder whenever only one pattern is well-represented in the signal; in such cases, one class classifier techniques are more indicated. The classification phase is applied after a preprocessing phase based on a multi layer model (MLM) that provides preliminary signal segmentation in an interval feature space. The one class KNN has been tested on synthetic and real (Saccharomyces cerevisiae) microarray data in the specific problem of DNA nucleosome and linker regions identification. Results have shown, in both cases, a good recognition rate.